Existing speech recognition approaches include interactive language proficiency testing systems using speech recognition. For example, an existing approach to automatic pronunciation evaluation is discussed in Bernstein et al., “Automatic Evaluation and Training in English Pronunciation,” ICSLP, Kobe, Japan (1990), the contents of which are incorporated herein by reference. Such an approach includes evaluating each utterance from subjects who are reading a pre-selected set of scripts for which training data has been collected from native speakers. In this system, a pronunciation grade may be assigned to a subject performance by comparing the subject's responses to a model of the responses from the native speakers.
Existing approaches also include, for example, using a hidden Markov model (HMM) based phone log posterior probability for scoring. A set of context independent models can be used along with HMM based phone alignments to compute an average posterior probability for each phone. The time alignment can be generated by viterbi, and the database can include 100 native speakers. Also, in such an approach, the log posterior probability of each frame can be discovered, summed up for all the frames aligned, and then time normalized by the duration. To generate a sentence score, the score for each phone is averaged over all the sentences.
Other existing approaches can also include, for example, a manually designed flow of questions (for example, an increasing difficulty level), a question selected to cover a wide variety (various sounds captured), assessment tools, and questions selected based on a static manner based on their discriminating power (for example, sensei grammar evaluation).